Operations | Monitoring | ITSM | DevOps | Cloud

AI Finds and Fixes Security Vulnerabilities | Harness Demo

What if AI could find a security vulnerability, determine whether it actually matters, write the fix, validate it, and open a pull request for review? In this demo, see how Harness Security Testing Agents work together to move from vulnerability detection to a verified fix with an audit trail along the way. The system uses three agents: Harness can analyze where vulnerabilities exist in your environment, determine whether they’re actually reachable, prioritize what needs to be fixed, and automatically create remediation pull requests.

The Five Levels of the Enterprise AI Software Factory

Scroll LinkedIn for ten minutes and you'd think every engineering organization already runs an autonomous SDLC, with agents writing, reviewing, and shipping code while humans watch. Inside large enterprises, the picture looks different. Coding agents there have to work around sensitive customer data, recurring compliance audits, a larger attack surface, downtime that costs millions, and a CFO who wants to know what last year's token spend bought.

Debug Production at the Speed of AI

Your coding agent can debug production issues now. Yes… Not just “help you debug.” Actually run the investigation… FOR YOU! In this new era of agentic development, speed matters. And in the old days (you know… last week or so) we used to investigate production bugs ourselves. Manually. Like humans. But for a lot of incidents, we don’t need to do all of that anymore.

Harness acquires Augment Code to advance the Autonomous SDLC

Harness Cosmos Software Factory Agent automates engineering from idea to code, connecting code context with delivery, security, testing, and production workflows. The world runs on software. Better healthcare, more accessible financial services, more efficient businesses, and better everyday experiences all depend on our ability to build and improve it. AI is making that faster, but the outcome that matters is not simply how much code we generate.

How we investigate Sentry errors with an AI agent

We built an AI agent on Qovery to investigate Sentry alerts before our team picks them up. Here’s how the workflow runs, what it delivers, and where engineers still need to step in. Rémi is a staff frontend engineer at Qovery. He writes about frontend architecture, developer experience, and building scalable UI systems for platform engineering tools.

The Infrastructure Puzzle: How AI-Enabled Event Management Software Powers Complex Event Programs

When registration, attendee data, check-in, communications, and reporting stop working in concert, complex programs break. A connected online event management software setup gives teams one operating layer, not yet another stack of tools that complicates work.

Generative AI in Banking: Balancing innovation, risk, and operational readiness

Generative AI (GenAI) is moving quickly into banking. According to a 2025 survey by McKinsey, 52% of financial institutions surveyed already consider GenAI a priority, while another 39% are interested but have not yet made it a top priority. As adoption grows, banks need to think carefully about what AI agents can access, which identities it uses, what actions it can take, and whether those activities can be traced when something goes wrong.

AI Investigation for ITOps: Faster Root Cause with Edwin AI

AI investigation for IT operations finds the likely root cause of an incident and shows the evidence behind it. This video covers how deep AI investigation works for ITOps, SRE, and platform engineering teams using LogicMonitor's Edwin AI. Engineers lose time stitching together alerts, logs, and recent changes after an alert fires. Deep AI investigation hands that first pass to AI agents, so your team starts closer to the fix. Edwin AI, LogicMonitor's AI agent for ITOps, correlates alerts, identifies root causes, and recommends remediation.